Can I calculate the training performance of GPUs by comparing their specification?
A lot matters when it comes to comparison of GPU's, I will give you a broad overview of the matter (it is not possible to go into exact details as huge number of factors are actually involved):


Cores - Number of CUDA cores increases means the parallelism has increased, thus multiple calculations can be done in parallel but is of no significance if your algorithm is inherently sequential. Then CUDA cores will not matter. Your library will parrallelize what it can parallelize and will take that many CUDA cores only, the rest will remain idle.
Memory - Memory is useful if you are working on data whose one instance requires huge memory (like pictures). Thus with greater memory you can load greater amount of data at the same time and the cores will process on that. If memory is too low then cores want data but it is not getting it (basically data available in RAM's is the fuel while cores are the engine, you cannot run jets on fuel tank the size of car, it will consume time to constantly refill the fuel). But according to convention of Machine Learning one should load only small mini-batches at a time.
Micro-architecture - Lastly, architecture matters. I do not exactly know how but NVIDIA's RTX is faster for deep learning than GTX. NVIDIA has two affordable architectures (Pascal - GTX and Turing - RTX). Thus even for exactly same specs Turing architecture will run faster for deep learning. But for more details you can explore NVIDIA's website on what architecture specialises in what. For example NVIDIA P-series is good for CAD purposes. Also there are some very high end GPU's using Tesla architecture.


So AFAIK, these are the factors that matter. The library you will be using also matters, as a lot depends on how the library unrolls your program and maps them on several GPU's. Also related these 2 answers I previously gave:

CPU preferences and specifications for a multi GPU deep-learning setup

Does fp32 &amp; fp64 performance of GPU affect deep learning model training?

Hope this helps!